The Surge of AI in Nepal

Nepal is at a pivotal moment in its AI journey. While many countries have already integrated AI into their key sectors, Nepal is beginning to explore AI’s potential in areas like healthcare, agriculture, and education. There is significant room for growth, and with this growth comes an increasing demand for skilled AI professionals who can lead and innovate.

Now is the perfect time to step into AI. With the rise of local AI-driven startups, tech companies, and global enterprises investing in Nepal, the opportunities for AI professionals are boundless. AI can address specific challenges in Nepal, and being a part of that change is a chance to make a meaningful impact.

Why Now is the Best Time to Learn AI

The demand for AI professionals is skyrocketing, not just globally but locally. As companies across industries seek talent to help drive AI innovation, the gap between demand and available skilled professionals grows. Now, more than ever, is the best time to equip yourself with AI skills and step into this exciting field.

AI is evolving quickly, and those who act now will be the ones leading the way. By joining the field today, you’re positioning yourself ahead of the curve. The future of AI is still being written, and you can be one of its key contributors.

Interested in joining the Fusemachines AI fellowship program Nepal 2025? Apply here.

How the Fusemachines AI Fellowship Program Can Help

So, how do you get started in AI? The Fusemachines AI Fellowship Program offers a comprehensive learning journey designed for those passionate about AI and eager to make a difference in Nepal.

The program begins by building a strong foundation in the core principles of AI and machine learning. From programming basics to essential math concepts, the fellowship provides a step-by-step approach, making it accessible even for beginners. Along with this, you’ll have the opportunity to work on real-world projects, tackling challenges relevant to Nepal’s specific needs.

The program is unique because it combines world-class education with practical experience. You’ll learn from industry experts who provide mentorship throughout your journey. The focus on hands-on learning ensures you gain the skills necessary to solve problems in Nepal’s rapidly evolving AI landscape.

The Hybrid Nature: Learning from Anywhere, Impacting Locally

One of the standout features of the Fusemachines AI Fellowship Program is also its hybrid approach. While you gain technical expertise, you also learn how to apply AI to solve real-world problems in Nepal. The program blends online learning with in-person sessions, offering the flexibility to balance studies with life commitments, while also immersing you in a supportive, collaborative community.

This flexibility is crucial for ensuring that individuals from rural and underserved communities have access to the same opportunities as those from urban areas. The program empowers you to contribute locally, whether it’s improving healthcare systems, innovating in agriculture, or creating AI-powered solutions for small businesses.

The AI Fellowship offers a full scholarship, ensuring that financial barriers don’t prevent you from accessing top-tier education. This is an essential aspect of the program’s mission to make AI education accessible to everyone, regardless of their financial background.

Your Path to Transformation

The program is more than just about acquiring technical skills. It’s about transforming into a confident AI professional capable of tackling challenges and creating innovative solutions. By the time you finish the program, you’ll not only have a solid foundation in AI but also the experience and confidence to apply your skills in the real world.

You’ll be empowered to:

• Apply AI to solve specific challenges faced by local communities in Nepal.
• Innovate across industries like agriculture, healthcare, and education.
• Contribute to Nepal’s growing AI ecosystem and position yourself as a leader in the field.

The program doesn’t just prepare you for a job—it prepares you for a career where you can truly make an impact.

The Future is AI—And It Starts Now

The AI field is evolving fast, and the future is full of possibilities. By entering the field now, you are positioning yourself to be part of this exciting wave of change. The Fusemachines AI Fellowship Program is designed to guide you through every step of the process—from learning the basics to applying your knowledge in impactful ways.

This is your moment to act. The time to learn and build your career in AI is now. Apply today and take the first step towards a career that will shape Nepal’s future.

The post Why the Time to Learn and Build Your Career in AI is Now appeared first on Fuse AI.

This comprehensive program identifies, nurtures, and develops AI talent in Nepal through rigorous training and hands-on learning. Here’s everything you need to know about the AI Fellowship Program.

What is the Fusemachines AI Fellowship Program?

The Fusemachines AI Fellowship Program is an accelerated learning program in Artificial Intelligence that runs for 23 weeks (20 weeks of coursework + 3 weeks of project work). This MicrodegreeTM program is developed by leading US university faculty members and AI industry experts, offering participants an opportunity to upskill in AI and Data Science.

With a 100% scholarship, fellows gain expertise in Machine Learning, Deep Learning, Generative AI, and AI deployment while working on real-world problems and community projects. Graduates will have a competitive edge in the job market and build a strong foundation for their AI careers.

What’s New in 2025?

• Expanded Generative AI Focus – Learn LLMs, Image Generation, and Prompt Engineering to stay ahead in AI advancements.
• Enhanced MLOps Training – Master deployment, monitoring, and ML as a Service for real-world AI applications.
• Community AI Project – Fellows will work on an AI-powered solution for a local problem, making an impact beyond the classroom.
• Exclusive Career Opportunities – AI Fellows will have access to special job openings at Fusemachines and partner companies.
• More Structured Application Process – A streamlined process with clear assessment stages for eligibility and selection.

Interested in joining the Fusemachines AI fellowship program Nepal 2025? Apply here.

To learn more about Fusemachines and its education programs, click here.

Program Structure

• Duration: 23 weeks (20 weeks of coursework + 3 weeks of project work)
• Method of Learning: Blended (Self-paced + Live Online Sessions)
• Time Commitment: 14 hours per week
  • 4 hours of live lectures
  • 10 hours of self-study, assignments, and projects
• Live discussions & guest lectures by academics and industry experts
• Assessments: Programming assignments, quizzes, case studies, and proctored exams
• Certification: Certificate of completion and recommendation letter (as needed)

Why Apply to the Fellowship Program?

If you are passionate about AI and data science, the Fusemachines AI Fellowship Program offers a transformative learning experience. Here’s why you should apply:

• Expert-led Curriculum: Designed by top AI instructors and industry experts.
• Hands-on Learning: Gain practical experience through assignments, real-world projects, and case studies.
• Industry-Relevant Skills: Master AI, ML, Deep Learning, Generative AI, and MLOps.
• Networking Opportunities: Connect with peers, industry professionals, and Fusemachines alumni.
• Placement Opportunities: Exclusive job openings for fellows at Fusemachines and partner companies.
• Full Scholarship: Receive high-quality AI education at no cost.

Program Eligibility

Applicants must meet the following criteria:

• Educational Background:
  • Open to 3rd-year engineering/IT students, graduates, and professionals.
  • Should have prior coursework in Linear Algebra, Probability, Statistics, and Basic Calculus.
• Programming Skills:
  • Proficiency in Python and familiarity with data structures, algorithms, and basic CS concepts.
• Soft Skills:
  • Fluent in English, good communication, teamwork, and a strong learning attitude.

All applicants must pass an exam covering Python programming, applied math, computer science concepts, and problem-solving.

AI Fellowship 2025 Program Outcomes

By completing the program, fellows will develop the following skills:

• Data Science & Machine Learning: Hands-on expertise in real-world AI applications.
• AI Algorithms & Deployment: Understanding of ML, Deep Learning, and Generative AI.
• End-to-End AI Pipeline: Model evaluation, tuning, and deployment.
• Community Impact: Develop an AI solution for a local problem.

Program Curriculum Highlights:

• Machine Learning: Regression, Classification, Clustering, Reinforcement Learning
• Deep Learning: CNNs, RNNs, GANs, Transformer Models
• Computer Vision: Object Detection, Image Segmentation, Image Generation
• Natural Language Processing: Text Extraction, Named Entity Recognition, Sentiment Analysis
• Generative AI: Large Language Models (LLMs), Image Generation, Prompt Engineering
• MLOps: Deployment, Monitoring, ML as a Service

AI Fellowship Journey: From Application to Enrollment

Here’s what to expect when applying:

Step 1: Online Application

• Submit general information, education/work background, and CV.
• Answer AI-related questions about experience and motivation.

Step 2: Online Proctored Entrance Exam

• Covers Python, Math (Linear Algebra, Probability, Statistics), CS fundamentals, DSA, Databases, and ML basics.

Step 3: Interview for Shortlisted Candidates

• Evaluates problem-solving skills, motivation, and program fit.

Step 4: Enrollment & Onboarding

• Selected fellows complete registration and get access to learning materials.

Key Dates for AI Fellowship 2025

• Applications Open: March 3, 2025
• Application Deadline: March 31, 2025
• Q&A Webinar: March 14, 2025
• Entrance Exam: April 5-9, 2025
• Interviews: April 14-18, 2025
• Classes Begin: April 28, 2025

Frequently Asked Questions (FAQs)

Is prior AI experience required?
No, but a background in Python, math, and basic CS concepts is necessary.

How long is the program?
23 weeks (20 weeks coursework + 3 weeks project work).

Are there any costs involved?
No. The program is 100% free, including training, assessments, and certification.

Are there job placement opportunities?
Yes. AI Fellows get priority placement at Fusemachines and exclusive career opportunities.

Can professionals apply, or is it only for students?
Yes, professionals, graduates, and 3rd-year students can apply.

What support is available after program completion?
Alumni receive networking opportunities, job placement support, and continued learning resources.

Bottom Line

Pursuing a career in AI can be challenging without the right guidance. The Fusemachines AI Fellowship Program offers a structured, immersive learning experience to help aspiring AI professionals gain the skills and confidence they need.

With an expert-designed curriculum, hands-on projects, and networking opportunities, this fellowship sets the stage for a thriving AI career. Don’t miss this chance to become an AI leader in Nepal!

Deadline for applications is March 31st, 2025.

The post All You Need to Know About Fusemachines AI Fellowship 2025 Nepal appeared first on Fuse AI.

Remember the cool filters that turn you into a cat on Snapchat? Or the art generator Dall-E that creates images based on simple input? AI is behind those too.

Maybe you’ve heard these stories and thought “AI sounds amazing, but it’s WAY too complicated for me.” Here’s a secret: you don’t need to be a tech wiz to understand AI.

With the right guidance, anyone can embark on an AI learning journey, starting from scratch. Imagine going from “AI? Huh?” to using it to solve real-world problems in Nepal. This is possible with the Fusemachines AI Fellowship Program.

This blog is your invitation to explore AI, overcome your doubts, and discover how you can turn your curiosity into valuable skills. So, put on your learning hat, get ready for a fun ride, and let’s begin your transformation from beginner to expert!

From Curious Newbie to Foundational Fighter

Remember feeling nervous and excited on your first day of school? Imagine starting an exciting new adventure, but instead of textbooks and classrooms, you’re surrounded by supportive peers and industry experts, all geared towards one goal: unlocking the world of AI.

That’s exactly what the initial phase of the Fusemachines AI Fellowship feels like. We know diving into AI can be overwhelming, so we start with building a strong foundation. Think of it as climbing a ladder, but each step equips you with the knowledge and skills to confidently take the next.

Here’s what “beginner’s steps” look like:

Building the basics: Remember building Lego castles as a kid? We start similarly, but instead of colorful bricks, we work with the building blocks of AI: programming basics (if needed), essential math concepts, and the core principles of machine learning. Don’t worry, even if you’re new to these terms, our experts will guide you patiently, step-by-step.

Learning by doing: Memorizing facts is cool, but applying them is even cooler! That’s why the program integrates hands-on projects from the very beginning. Imagine building your own mini AI program or analyzing real-world data—all while having experts by your side to answer your questions and celebrate your progress.

A supportive community: Remember those nervous first-day jitters? Well, forget them! You’ll be surrounded by fellow AI enthusiasts, just like you, creating a supportive learning environment. Ask questions, share ideas, and learn from each other.

This initial phase might seem basic, but trust us, it’s crucial. You’ll be amazed at how quickly you progress from a complete beginner to someone confidently navigating the fascinating world of AI. And that’s just the beginning of your incredible journey!

From Foundations to Future-Ready: Shaping Your AI Journey

The next step in your AI journey takes you beyond fundamentals as you begin crafting your own personalized skillset, tailored to address Nepal’s unique needs and opportunities.

This phase empowers you to:

Dive deeper into AI concepts: Explore areas that fascinate you, be it natural language processing, computer vision, or machine learning algorithms.

Shape your learning path: Collaborate with mentors and peers to design projects that challenge and inspire you, applying your skills to problems you find meaningful.

Develop a Nepal-focused perspective: Gain insights into the country’s unique challenges and opportunities, ensuring your AI knowledge has direct relevance and impact.

Engage in hands-on projects: Tackle real-world challenges alongside organizations and communities in Nepal, gaining practical experience and making a difference.

Receive expert guidance: Learn from industry professionals who share their knowledge and help you navigate the world of AI.

Build a supportive network: Connect with like-minded individuals and experts, fostering collaboration and ongoing learning within the AI community.

By the end of this phase, you won’t just be an AI enthusiast; you’ll be a future-ready AI practitioner, equipped with the tools, skills, and Nepal-focused perspective to:

• Contribute to innovative solutions for Nepal’s challenges.
• Become a leader in shaping the future of AI in your community.
• Embark on a fulfilling career that makes a lasting impact.

Transformation and Expertise: Your AI Journey Starts Now

Ready to transform your curiosity into AI expertise and contribute to a brighter future for Nepal? Imagine yourself joining a supportive community of aspiring AI professionals, guided by industry experts, and equipped with the skills to tackle real-world challenges. This is the transformative power of the Fusemachines AI Fellowship Program.

More than just learning AI:

The program goes beyond teaching technical skills. It fosters:

Problem-solving mindset: Identify and analyze real-world challenges unique to Nepal, developing innovative AI solutions that make a tangible impact.

Critical thinking and innovation: Push the boundaries of AI applications, exploring creative solutions tailored to your community’s needs.

Growth mindset: Embrace continuous learning and adapt to the ever-evolving world of AI, becoming a lifelong learner and leader in the field.

Your transformation story can begin today. Applications for the next cohort of the Fusemachines AI Fellowship Program are now open! Apply now!

Here’s your chance to:

Learn from the best: Gain mentorship from industry experts and renowned faculty, acquiring practical knowledge and industry insights.

Collaborate with peers: Join a vibrant community of aspiring AI professionals, fostering learning, support, and lifelong connections.

Tackle real-world projects: Apply your skills to hands-on projects, addressing Nepal-specific challenges and making a difference in your community.

Bottom line

Don’t just read about transformation, embrace it. Join the Fusemachines AI Fellowship and unlock your potential to impact Nepal’s future. Apply now, applications close on [date]. 

The post From Beginner to Expert: Your AI Learning Journey appeared first on Fuse AI.

After all, every company must progress in this industry because every company offers a product that provides support to users. Consumers use technology and link to network providers in order to “stay connected” and wield the power of looking up almost any information in the palm of their hands. Such product lines include Siri, Google Allo, and Bixby, among many others. There are also a plethora of goods from different companies on the market that use AI methods to help users obtain relevant data or complete required tasks.

Samsung and AI

Similarly, Samsung also uses AI to provide personalized content suggestions to users. The Samsung Research Center has seven AI centers in five countries: Korea (Seoul), Canada (Montreal and Toronto), Moscow, the United Kingdom (Cambridge), and the United States (New York and Silicon Valley), all working towards providing its clients with fresh AI-based insight and value. Several of the brand’s most recent products have also used Machine Learning (ML) to allow users to enjoy and explore cutting-edge future generation AI techs.

In like manner, along with Samsung, Apple and Huawei have also imbued their smartphones with AI chips capable of doing nearly 5 trillion operations per second while consuming significantly less power.

At a forum session in 2020, Samsung presented its long-term humanist vision for its AI strategy. The company aspires to provide purpose-built applications that improve end-user experiences. Rather than chasing new-fangled technology, Samsung is more interested in emphasizing personalization, seeking to assist its users in discovering items that meet their needs and budgets while also providing exclusive experiences.

Listed below are some ways AI in Samsung Products is used.

Samsung’s Virtual Assistant Bixby

Like all virtual assistants, Bixby’s goal is to make customers’ lives easier and more convenient. It does so by assisting them in completing activities and answering inquiries by understanding language and speech patterns based on their requests.

Bixby’s features include:

• Bixby Routines: Bixby observes exclusive behavior patterns and suggests an automated task routine.
• Making Enhanced Predictions: As per the received input, Bixby guesses the user’s requirements and offers personalized recommendations.
• Managing the User’s House: Bixby can switch TV channels, switch house lights on and off, and also control the thermostat.
• Quick Command: Once the user sets a trigger command, Bixby can execute a set of tasks in a particular order. These commands can include generating a command for a “morning routine,” which can incorporate sharing the weather report or playing a morning playlist.

Household Robots

Samsung Bot Handy was unveiled as an AI-powered domestic robot during the Consumer Electronics Expo in January 2021.

Although the robot is still in development, it has been designed to perform chores and lend a helping hand around the house. The robot may help the user by doing laundry, filling the dishwasher, setting the table, arranging groceries and laundry, and even pouring a glass of wine.

Smart TVs

AI enables Samsung TVs to provide viewers with personalized content recommendations. Meanwhile, NLP allows users to control volume, apps, and channels with voice requests.

Likewise, Machine Learning is also constantly improving the resolution quality of the platform’s Smart TVs. AI upscales the resolution to 8k to provide viewers with an engaging picture and an excellent viewing experience.

Smartphone Cameras

Samsung’s AI-powered smartphone cameras have improved visual detection capabilities, allowing users to shoot high-quality photos while having fun.

Samsung cameras also shoot better portrait photos. It does so because of its capacity to recognize faces. The AI can also optimize exposure and skin tones in changing lighting conditions. Additionally, the AI also aids in storage by allowing saved pictures to be categorized, allowing users to locate the images they are seeking as they browse quickly.

You can also read our previous blog that details how Google uses AI: How Google uses AI to Improve Search.

Conclusion

AI and Machine Learning can revolutionize technology. AI in Samsung products and other leading manufacturers continuously integrate ML and AI algorithms in fresh and appealing ways. Doing so offers enhanced experiences for their customers. It also helps maintain its place amidst the competition.

The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. The proprietary curriculum includes Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision courses. Certifications like these will help engineers become leading AI industry experts. It also aids them in achieving a fulfilling and ever-growing career in the field.

The post AI in Samsung Products appeared first on Fuse AI.

Similarly, AI breakthroughs and developments in Machine Learning (ML) will also continue to push boundaries, similar to how steam, electricity, and computers ushered in the first three industrial revolutions. 

Here are the top 22 AI trends in 2022.

AI Engineering

AI Engineering will be at the forefront of future AI trends. The staying power and lasting value of AI investments have been tremendous across many companies. In like manner, as the market for AI innovations grows, efforts into AI models will also expand to drive investments. In fact, future trends lean towards the mass adoption of AI engineering, leading to three times the value for AI efforts.  

You can also check our previous blog to know why AI Engineering is one of the most high-in-demand career prospects in the market today: What is AI Engineering and Why You Should Join this Field.    

Web 3.0

Web 3.0, or “Semantic Web,” is where the web will be used as a database incorporated with intelligent search engines, efficient filtering tags, and digitized information. Consisting of AI-enabled services, Web 3.0’s decentralized data architectures and edge computing will make it one of the biggest AI trends in 2022. 

AI in Healthcare

The healthcare industry is among the primary economic sectors that will continue to evolve as Machine Learning and AI in technology become more prevalent. Current AI trends already include AI-enabled machines being as good as human experts when it comes to diagnosing disease from medical images. Moreover, current Deep Learning software also show enormous promise in diagnosing a range of diseases, including cancer and eye conditions.

AI trends in 2022 in healthcare will include researchers developing AI models that can predict the development of breast cancer years in advance. Crucially, the system will be created to work well for diverse patients. Similarly, another trend that can quickly become a global standard in the near future is Infervision’s image recognition technology that will use AI to look for signs of lung cancer in patient scans.

AI in Cybersecurity

Hacking and cybercrime have inevitably become more of a problem as machines take up more of our lives. Every device connected to a network inevitably becomes a potential point-of-failure that hackers could exploit. As a matter of fact, the World Economic Forum identified cybercrime as potentially posing a more significant risk to society than terrorism.

It is a given then that potential AI trends in 2022 and beyond will focus on cybersecurity. Identifying points of failure becomes more complex as networks of connected devices become more complex, and this is where AI can play a role. Smart AI algorithms will play an increasingly major role in keeping cyber-crimes at bay by analyzing network traffic and learning to recognize patterns that suggest nefarious intentions.

Simultaneously, a significant AI application in cybersecurity in 2022 includes the Cybersecurity mesh. It is a form of architecture that provides an integrated approach to IT security assets no matter the location. It will consequently redefine the perimeters of cybersecurity as it will provide a more standardized and responsive approach to people’s identities or things. This is a pathway to reduce the financial implications of cyber incidents by almost 90%.

Hyper Automation

Automation enables technologies to produce and deliver goods and services with minimal human intervention. The current implementation of automation in technologies and techniques has already improved the efficiency, reliability, and speed of tasks previously performed by humans. As such, automation is critical for digital transformation. 

Likewise, Hyper Automation means faster identification and automation across enterprises. It will improve work quality, hasten business processes, and foster decision-making. Thus, as new innovations emerge, Hyper Automation will be on the rise, which is why it is one of the growing AI trends in 2022.

Augmented Workforce

Many companies embrace the process of creating data and AI-literate cultures within their teams. As time goes on, this will become the norm, with the human workforce working with or alongside machines with cognitive functionality. 

In many sectors, AI-enabled tools are already used to determine leads that are worth pursuing. The tools also convey the value businesses can expect from potential customers. For example, in engineering, AI tools provide predictive maintenance. Likewise, in knowledge industries such as law, AI-enabled tools help sort through a growing amount of data to find valuable information.

Generative Artificial Intelligence (AI)

Generative AI algorithms use existing content, such as text, audio files, or images, to create new content. In other words, it enables computers to use abstract and underlying patterns related to the input to generate similar content. There has been an increase in interest and investment in generative AI over the past year. By the same token, predictions include generative AI accounting for 10% of all data production in the next three and a half years, a significant increase from the current 1%. 

AI in Entertainment  

Current AI-enabled content platforms, such as Netflix and Spotify, use AI to understand what viewers want to watch or listen to and make personalized recommendations. As new AI-enabled innovations emerge, more of such similar tools and services will become popular. Some examples of trendy AIs in the entertainment sector include search engines, such as China’s Sogou, capable of creating an AI that can read novels aloud, simulating the author’s voice (similar to how Deepfakes can create realistic audio and video content). Other examples include AI-enabled tools, such as Sony’s AI DrumNet, which produces drum beats.

Data Fabric

A data fabric is an architecture that serves as an integrated layer (fabric) of data and connecting processes that provide consistent capabilities across a choice of endpoints spanning hybrid multi-cloud environments. In adjacent, it also standardizes data management practices across cloud and devices, fostering resilient and flexible data integration across platforms. Additionally, the standardization can also lead to significantly reduced data management efforts while also substantially improving time to value. 

Better Language Modeling

Language modeling allows machines to understand and communicate with humans in our spoken languages. Simultaneously, it enables the translation of natural human languages into computer codes that can run programs and applications. An example is GPT-3 by OpenAI, the most advanced language model ever created. This model consists of around 175 billion parameters (variables and data points machines use to process language). A future AI trend includes OpenAI’s successor, the GPT-4, predicted to be even more powerful with 100 trillion parameters, making it 500 times larger than GPT-3. 

Intelligent Consumer Goods 

Smart consumer goods aim to simplify mundane tasks by getting to know one’s preferences and behavior to anticipate needs and respond accordingly. It works similarly to AI-enabled tools and services that fall within the entertainment sector. Examples of smart consumer goods include Google’s Nest’s thermostat, which tracks how people use their homes so that it can regulate the temperature. Similarly, the Orro intelligent light switch can detect when someone enters a room and switch the lights on and off. 

Autonomic Systems

Autonomic systems with built-in self-learning can dynamically optimize business performance, and protect against cybercrimes. This trend anticipates greater levels of self-management of software.

AI and the Metaverse

A unified digital environment, the metaverse, is a virtual world much like the internet, where users can work and play together. It emphasizes enabling immersive experiences often created by users themselves. AI will be a significant player in the metaverse, helping create online environments where humans can nurture their creativity. An example of a metaverse is depicted in the 2021 Ryan Reynolds movie “Free Guy.” 

Decision Intelligence (DI)

Decision Intelligence is a discipline of AI engineering that augments data science with social science, decision theory, and managerial science. DI applications provide a framework for best practices in organizational decision-making. It also aims to hasten decision-making by modeling decisions in a repeatable way to increase efficiency. It’s predicted that one-third of large enterprises will use DI for better and more structured decision-making in the next two years.

IoT in Business

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals, or people provided with unique identifiers (UIDs). These interrelated units have the ability to transfer data over a network without human-to-human or human-to-computer interaction. The IoT allows businesses and companies to make and sell products by making them smart and delivering unprecedented insights into product use. These insights allow companies to deliver better services and products. 

The IoT gives businesses the chance to deliver customer value propositions and generate income streams. Data generated from IoT devices are a vital business asset and can bolster a company’s value. For many companies, the most prominent IoT opportunities are data generated from smart machines. The data can improve company operations and reliability, and reduce costs.

Read our previous blog about how AI applications can benefit businesses: 10 Benefits and Applications of AI in Business.

Composable Applications

Composable applications highlight functional blocks of an application that can be decoupled from overall applications. These individual parts can be more finely tuned to create new applications. Companies that can leverage composable applications are predicted to outpace the competition by 80% regarding feature implementation, making it one of the notable AI trends in 2022 in business.

Low-code and No-code AI

No-code and low-code solutions offer simple interfaces to bypass the AI talent demand gap. These interfaces can be used to construct increasingly complex AI systems. No-code AI systems will enable the creation of smart programs by plugging together premade modules. These modules can then be fed domain-specific data, much like how web design and no-code UI tools, such as Wix or Squarespace, let users create web pages and other interactive systems by dragging and dropping graphics. Natural Language Processing (NLP) and Language Modeling may make it possible to use voice or written instructions to create programs. This will play a vital role in the democratization of AI and data technology.

Cloud-Native Platforms (CNPs)

Cloud-Native Platforms will provide the foundation for most digital initiatives by mid-decade. These platforms leverage cloud technology to offer IT-related capabilities. Subsequently, they reduce vendor lock-ins by giving users a choice of tools without being stuck with legacy offerings. Cloud-Native Platforms are more portable and beyond the reach of predatory vendor pricing as they run on multi-cloud compatible tooling. Invisible infrastructure equals easy portability. Using the cloud for storage offers access to files from anywhere with an internet connection. Files can still be accessed in the event of a hard drive failure or other hardware malfunction. CNPs act as a backup solution for local storage on physical drives.  

Autonomous Vehicles

AI will guide autonomous cars, boats, and aircraft set to revolutionize travel and society over the coming decade. Tesla says its cars will demonstrate full self-driving capability by 2022. Accordingly, we can expect competitors Waymo, Apple, GM, and Ford to announce significant leaps forward in the next year. 

Privacy Enhancing Computation (PEC)

Data and information privacy is an increasingly important concern. Privacy Enhancing Computation (PEC), accordingly, protects the confidentiality of a company and its customers’ data. Reducing privacy-related risks consequently helps maintain customer loyalty. As a matter of fact, it is estimated that roughly 60% of large enterprises will leverage PEC practices by 2025. 

Creative AI

We have used AI to create art, poetry, music, plays, and even video games. Popular examples include the paintings of Pindar Van Arman and the music of Taryn Southern. Moreover, we can expect even more elaborate and fluid creative outputs as new models, such as GPT-4 and Google’s Brain, redefine boundaries. We can similarly expect to see AI applied to routine creative tasks, such as writing headlines for articles and newsletters and designing logos and infographics. 

Non-Fungible Tokens (NFT) 

A Non-Fungible Token (NFT) is a non-interchangeable unit of data saved on a digital ledger (blockchain). NFTs can be used to represent reproducible items such as photos, videos, audio, and other types of digital files, as unique items. In the same way, 2022 will see companies dabbling in the creation of NFTs for a fee. We have already seen this in the arts and entertainment. We will also likely see the emergence of more tokenization marketplaces.

In conclusion, the fourth industrial revolution offers enormous opportunities to make the world a better place. Equally important is the proper use of these technologies. They can address some of the world’s biggest challenges – from climate change and inequality to hunger and healthcare. As a result, these technologies will change businesses, reshape business models, and transform industries. 

The post Top 22 AI Trends in 2022 appeared first on Fuse AI.

“AI is a concept bigger than ML, used to create intelligent machines capable of simulating human thinking capability and behavior. Machine Learning, on the other hand, is an application or subset of AI that enables machines to learn from data without being explicitly programmed. In other words, AI is the all-encompassing concept that initially erupted, followed by ML that thrived after.” 

AI vs ML – Major Differences (and Overview)

Artificial Intelligence

Artificial Intelligence studies methods to build intelligent programs and machines to creatively solve problems. It has always been considered the human prerogative.

As such, AI aims to build computer systems that mimic human intelligence. The term “Artificial Intelligence”, thus, refers to the ability of a computer or a machine to imitate intelligent behavior and perform human-like tasks.

Likewise, these tasks include actions such as thinking, reasoning, learning from experience, and most importantly, making decisions. AI systems do not require pre-programming. Rather, they use algorithms.

There are many well-known examples of AI, such as Siri, Google’s AlphaGo, and AI in Chess playing, among many others. 

AI Classification

 AI is classified into three types based on capabilities: Weak AI, General AI, and Strong AI.   

Artificial Narrow Intelligence (ANI) or Weak AI 

Weak, or Narrow AI, performs particular tasks but is incapable of passing as a human outside its defined capacities. Most AI in use today is categorized as Weak AI. It is widely used in science, business, and healthcare.

One of the earliest examples of Weak AI is Deep Blue, the first computer to defeat a human in chess. (Not just any human either: Deep Blue defeated Garry Kasparov in 1996). Another good example of Weak AI is bots. 

Bots are software capable of running simple, repetitive, and automated tasks, such as providing answers to questions such as, “How is the weather?” or “What are some good burger restaurants near me?” Bots pull data from larger systems, such as weather sites or restaurant recommendation engines, and deliver the answer. 

Artificial General Intelligence (AGI) 

Artificial General Intelligence systems perform tasks that humans can with higher efficacy, but only for a particular/single assigned function. They are incapable of doing tasks not assigned to them.

Thus, AGI systems can make decisions and learn without human input. Engineers program AGI machines to produce emotional verbal reactions in response to various stimuli. Examples include chatbots and virtual assistants capable of maintaining a conversation. 

Artificial Super Intelligence (ASI) or Strong AI

Strong Artificial Intelligence is the theoretical next step after General AI, perhaps more intelligent than humans. Right now, AI can perform tasks, but they are not capable of interacting with people emotionally.

Additionally, if you want to know more about AI and its subsets, you can check this blog: What is AI? Artificial Intelligence and its Subsets.

Machine Learning (ML)

Machine Learning is a subset of Artificial Intelligence that deals with extracting knowledge from data to provide systems the ability to automatically learn and improve from experience without being programmed. In other words, ML is the study of algorithms and computer models machines use to perform given tasks. 

There are different types of algorithms in ML, such as neural networks, that help solve problems. These algorithms are capable of training models, evaluating performance and accuracy, and making predictions. Furthermore, ML algorithms use structured and semi-structured data. They also learn on their own using historical data. 

The “learning” in ML refers to a machine’s ability to learn based on data. You can say that ML is a method of creating AI. Additionally, ML systems also recognize patterns and make profitable predictions.

Many fields use Machine Learning, such as the Online Recommender System, the Google Search Algorithm, Email Spam Filters, and Facebook Auto Friend Tagging Suggestion.

Components of Machine Learning 

Datasets: ML Engineers train systems on special collections of samples called datasets. The samples can include texts, images, numbers, or any other kind of data. Usually, it takes a lot of time and effort to create a good dataset.

Features: Features are important pieces of data that function as key components to the solution of tasks. Features demonstrate to the machine what to pay attention to.

Algorithm: Machine Learning algorithms are programs, like math or logic. An algorithm can adjust itself to better performance when exposed to more data. It is a procedure that runs on data to create a machine learning model. Essentially, they perform pattern recognition. Similarly, the accuracy or speed of getting results differs from one ML model to the next depending on the algorithm. 

When it comes to performing specific tasks, software that uses ML is more independent than ones that follow manually encoded instructions. An ML-powered system can be better at tasks than humans when fed a high-quality dataset and the right features. 

Types of Machine Learning 

Supervised Learning

In Supervised Learning, an ML Engineer supervises the program throughout the training process using a labeled training dataset. This type of learning is commonly used for regression and classification. 

Examples of Supervised ML include Decision Trees, Logistic Regression, Naive Bayes, Support Vector Machines, K-Nearest Neighbours, Linear, and Polynomial Regression. 

Hence, Supervised ML is commonly used for language detection, spam filtering, computer vision, search, and classification. 

Semi-Supervised Learning

Semi-Supervised Learning uses a mixture of labeled and unlabeled samples of input data. In this process, the programmers include the desired prediction outcome. The ML model must then find patterns to structure the data and make predictions.

Unsupervised Learning

In Unsupervised Learning, engineers and programmers don’t provide features. Rather, the model searches for patterns independently. Therefore, this type of ML is good for insightful data analytics. The program can recognize patterns humans would miss because of our inability to process large amounts of numerical data. 

That being so, UL can be used to analyze customer preferences based on search history, find fraudulent transactions, and forecast sales and discounts. Examples include K-Means Clustering, Mean-Shift, Singular Value Decomposition (SVD), DBSCAN, Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), Latent Semantic Analysis, and FP-Growth. 

Accordingly, engineers commonly use them for data segmentation, anomaly detection, recommendation systems, risk management systems, and fake images analysis.

Reinforcement Learning

Finally, Reinforcement Learning is an ML training method formulated on rewarding desired behaviors, and/or punishing undesired ones. This is very similar to how humans learn: through trial. A reinforcement learning model is capable of perceiving and interpreting its environment, taking actions, and learning through trial and error. 

Furthermore, RL allows engineers and programmers to step away from training on static datasets. Instead, the computer is capable of learning in dynamic environments, such as in video games and the real world. Reinforcement learning works well in in-game research as they provide data-rich environments. 

Some examples include Q-Learning, SARSA, Genetic algorithm, DQN, and A3C. As such, engineers and programmers commonly use them for self-driving cars, games, robots, and resource management.

AI vs ML – Key Differences

We use AI to resolve tasks that require human intelligence. ML, on the other hand, is a subset of AI that solves specific tasks by learning from data and making predictions. For this reason, you can say that all Machine Learning is AI, but not all AI is Machine Learning.

The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. Likewise, the proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these help engineers become leading AI industry experts, aiding them in achieving a fulfilling and ever-growing career in the field.

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Evolving leaps and bounds from when Alan Turing first explored the mathematical possibility of AI, this enigmatic field of computer science has become an integral part of today’s world at large. This fascinating field is progressing rapidly into diverse sectors of modern civilization.     

We interact with AI in many ways, whether with Siri and Alexa, or through other forms like smartphones, social media feeds, and music and media streaming services. Currently, more and more organizations are putting assets into Machine Learning. Many are also demonstrating a powerful development in AI items and applications.

What is AI?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines. The engineers program the machines to mimic human actions- such as prediction and decision making. One of the core characteristics of an Artificially Intelligent machine is its capacity to rationalize and take actions that procure the best chance of obtaining a specific goal.

According to B.J Copeland, professor of philosophy and director of the Turing Archive for the History of Computing, University of Canterbury,

“Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”   

Hence, Artificial Intelligence intelligently executes tasks that capitulate in creating extensive accuracy, flexibility, and productivity for the entire system. In like manner, AI Engineers also navigate the inner workings of the organization to create and deploy practical models for real-world use. To know more about the responsibilities of an AI Engineer, you can check this blog- Responsibilities of an AI Engineer.    

Subsets of Artificial Intelligence (AI)

There are a plethora of methods that fall under the space of Artificial Intelligence, for example- linguistics, bias, vision, robotics, planning, natural language processing, decision science, etc. Let us look into 5 major subsets of Artificial Intelligence:

• Machine Learning (ML)
• Neural Network (NN)
• Deep Learning (DP)
• Robotics
• Computer Vision (CV)

Machine Learning (ML)

Modern computer programs can automatically adapt to, and learn from, new data without human assistance. This is what Machine Learning is. This process functions through Deep Learning techniques. Computer programs can automatically learn by absorbing large amounts of unstructured data such as images, texts, or videos. ML is perhaps the most applicable subset of AI to the average enterprise today.  

Neural Network (NN)

The Neural Network (NN) is a part of Artificial Intelligence that utilizes nervous system science (a piece of biology that deals with the nerve and nervous system of the human cerebrum) to merge the aspects of cognitive science with machines so they can perform tasks. Neural Network and Machine Learning combinedly tackle numerous intricate tasks effortlessly. 

Deep Learning (DP)

Chris Nicholson, CEO of Pathmind, offers a valuable analogy: Think of a lot of Russian dolls settled within one another. “Deep Learning is a subset of Machine Learning, and Machine Learning is a subset of AI, which is an umbrella term for any computer program that accomplishes something smart.”

Deep Learning brings into service the alleged neural systems, which learn from processing the labeled information provided during training. It then uses this answer key to realize what attributes of the information are expected to build the right yield. Deep Learning powers product and content recommendations for Amazon and Netflix. Furthermore, it also works in the background of Google’s voice-and image-recognition algorithms.

Robotics 

Robotics is a fascinating division of the innovative field. This subset of Artificial Intelligence focuses on the design and development of robots. Robotics is an interdisciplinary field of science and engineering that combines aspects of mechanical engineering, electrical engineering, computer science, and numerous other fields. It decides the production, design, operation, and use of robots. Additionally, it also manages computer systems for their control, intelligent results and data change.

Computer Vision (CV)

According to IBM– Computer Vision is a field of Artificial Intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs. Computers can then take actions or make recommendations based on that information. If Artificial Intelligence enables computers to think, Computer Vision enables them to see, observe and understand. 

AI Applications 

There are many sectors that use AI. Artificial Intelligence and like programs are extensively used for research in the medical field to improve the accuracy of programs that detect health conditions and in the creation of innovative technologies such as autonomous vehicles. 

Artificial Intelligence is also used in popular programs such as Netflix and Spotify. This type of AI monitors user habits and makes recommendations based on recent activity. Banks use AI systems to monitor member activities to check for identity theft, maintain online security and approve loans. One can find such AI systems in call centers as well. The programs analyze a caller’s voice in real-time to provide information to the call center to help build a faster rapport. 

Artificial Intelligence is creating advanced technologies in various fields which in turn creates a more efficient world. When you look at the environment today, you will find that AI-enabled machines are involved in various roles involving transportation, medical procedures, military applications, and even industrial and commercial fields. 

As such, the field of Artificial Intelligence is extremely lucrative and offers many career opportunities. To know more about the skills required to become an AI Engineer, you can check this article- Skills Required to become an AI Engineer.  

Conclusion 

There is no scarcity of challenges that need to be solved today to enable a better tomorrow for our society and the planet. Collaboration between humans and Artificial intelligence can lead to solutions that otherwise wouldn’t have been thought of. These solutions then can be developed and vetted at a pace that wouldn’t be possible if only humans were working on it without AI. Proper use of Artificially Intelligent machines can provide innovative ways of solving extremely challenging problems. It can also provide ways of significantly improving life. AI Engineers are always looking for innovative ways to produce ingenious products that enhance our lives. Check this article that elaborates on what AI Engineering is- What is AI Engineering.    

The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. The proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these will help engineers become leading AI industry experts, and also aid them in achieving a fulfilling and ever-growing career in the field.  

The post What is AI? | Artificial Intelligence and It’s Subsets appeared first on Fuse AI.

Similarly, while AI Engineering is a relatively new field, it is becoming more and more relevant in many economic sectors, especially in the industrial and manufacturing industries. Not only that, but AI Engineering is also becoming relevant in healthcare, security, finances, and entertainment. 

If you want to know more about why AI Engineering is important today, check out this article: What is AI Engineering and Why Should You Be One.  

Some of the most interesting and meaningful applications of AI are in engineering and more and more people are entering the field. 

Roles and Responsibilities of an AI Engineer 

In fact, AI Engineers are responsible for using programming algorithms so they can assemble, test, and convey AI models. Likewise, AI Engineers are problem-solvers; it is their responsibility to ensure that AI systems or infrastructures are properly implemented. 

AI Engineers build, deploy and maintain AI-based systems. Working closely with businesses and tech companies, AI Engineers help improve company operations, enhance products or software development, and make proper service delivery, among others. 

Furthermore, if you want to learn more about the technical skills required to become an AI Engineer, check out this article, Skills Required to Become an AI Engineer.  

Similarly, AI Engineers are primarily responsible for implementing Machine Learning (ML), a process that involves the application of Artificial Intelligence to automatically perform a specific task without explicitly programming it. 

Other obligations include: 

• Creating and managing the AI development process and the overall infrastructure of the product
• Directing factual investigation and deciphering outcomes so that they can guide the organization’s decision-making process
• Mechanizing framework for the data science team 
• Creating foundations for information change and ingestion 
• Building AI models 
• Explaining their AI models to stakeholders
• Changing the AI models into APIs different applications can communicate with

In addition, aside from the responsibilities above, the core responsibility and duty of an AI Engineer is to navigate through the AI Framework. For the most part, AI Engineers focus on learning how to build and deploy AI models. However, the most essential skill to master is the complete understanding of AI/ML workflow. This workflow system includes- 

• Data Preparation
• AI Modeling
• Simulation 
• Testing and Deployment 

The AI-Driven Workflow 

Correspondingly, as an AI Engineer, you need to know that AI is not just modeling, but also a series of steps: data preparation, modeling, simulation and test, and deployment.

Step 1: Data Preparation

Firstly, Data preparation is the process of feeding accurate data as input to train a model. If engineers do not provide accurate data, projects are more likely to fail, and engineers will not get accurate or helpful results. To train a model, AI Engineers should begin with large amounts of clean, labeled data. This is also one of the most time-consuming steps of the workflow.

An AI model is likely to fail without accurate training data. AI Engineers should know data classification procedures, the clustering process, the task of regression, human insights, data cleansing activities, and discretion proceedings.   

Step 2: AI Modeling 

Secondly, after cleaning the data and properly labeling it, comes AI modeling. This is where data is used as input, and the model learns from that data. The goal is to create an accurate model that can make intelligent decisions based on data. This is also where Deep Learning, Machine Learning, or a combination thereof comes in.

AI Engineers must learn how to create accurate models that can make decisions based on data. For this, AI Engineers would use the concepts of Deep Learning (Neural Networks), and Machine Learning (Decision Trees). 

Step 3: Simulation 

The model does not exist in a vacuum and must work with all other pieces in the system. Let us take an example of an automated driving scenario. Not only does the self-driving car have a perception system for detecting objects (cars, pedestrians, stop signs), but the vehicle’s perception also has to integrate with other systems for path planning, localization, controls, and more. 

The key to a successful model is proper simulation and testing. Indeed, it is only when an AI model passes the simulation and testing phase that it is deployed into the real world. 

Step 4: Deployment 

The final step is Deployment. Engineers need to master making an implementation-ready model that can be fitted into a designated hardware environment. Accordingly, once the model is ready to deploy, the next step is the target hardware. In other words, deployment is the step in which the AI Engineer readys the model in the final language in which it will be implemented. In this step, the design engineers share an implementation-ready model which fits into the designated hardware environment. The designated hardware environment can range from desktops to the cloud to FPGAs.

Conclusion 

The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. The proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these will help engineers become leading AI industry experts, and also aid them in achieving a fulfilling and ever-growing career in the field.  

The post AI Engineer Responsibilities- What are They? appeared first on Fuse AI.

How to Become an AI Engineer

There are many ways to become an AI developer. Most, if not all, AI jobs require an analytical thought process. A key characteristic is the ability to solve problems with cost-effective and efficient solutions. AI Engineers should be aware that technological innovations need to be able to translate to state-of-the-art programs. 

To elaborate, let’s dive into innovations. It is apparent, initial product innovation first needs an introduction of a new product. You cannot, however, introduce just any new product. The new product must solve an existing problem in an exciting and modern way. Or the product needs to solve a completely new problem that has come to light.

For example, at the 2016 Consumer Electronics Show, the electronics company LG introduced a new type of flexible screen that you can roll up like a newspaper. When you first hear it, it sounds like science fiction. But it’s not in the realm of fiction anymore, it’s an actual real product that could become readily available to everybody in a short time. So what’s innovative about this, you ask? This innovative product solves the portability problem- instead of large, unwieldy screens, people can instead show videos on screens they can fold up when they are done and put in their bags. 

New innovative products can introduce new technologies or new ways to do something. And this is what AI Engineers need to be aware of- translating technological innovations into tangible practical products. 

The following topics provide an understanding of the prerequisites that you must obtain to get the AI job of your dreams.  You can also check this blog that elaborates on what AI Engineering is- What is AI Engineering. If you are hoping to begin a career as an AI Engineer, here are the requirements. 

Education Prerequisites Required to be an AI Engineer 

You first need to earn a bachelor’s degree to become an AI Engineer. You can earn a degree in the following subjects:

• Computer Science 
• Mathematics 
• Information Technology 
• Statistics 
• Finance 
• Economics 
• Data Science
• Cognitive Science

You can also take additional courses and pursue relevant certifications. There are many online courses and certifications created to further your AI knowledge and skills. For example, you might consider taking the AI2GO programming course by Fusemachines, an online course designed for individuals to learn about AI and its applications with no computer background knowledge necessary. 

Technical Skills Required to Become an AI Engineer

You will need in-depth knowledge of various technical skills to be a successful AI Engineer. You must know and use various software development techniques and practices, along with programming skills. As an AI Engineer, you will have a set of responsibilities within your workplace. To know more about the responsibilities of an AI Engineer, you can check out this article- What are the Responsibilities of an AI Engineer? 

Make sure you have a firm grasp of the following topics: 

• Programming Languages
• Statistical Knowledge
• Applied Maths and Algorithms
• Natural Language Processing
• Deep Learning and Neural Networks

Programming Skills 

The first skill you need to become an AI Engineer is programming. Learning programming languages such as Python, R, Java, and C++ is a must to build and implement models. It is also good to know about classes and data structures. In addition, you might encounter projects where you need to leverage hardware knowledge. Thus, you must be familiar with basic algorithms, classes, memory management, and linking. 

Applied Math and Statistical Skills

Technical and statistical skills include matrices, vectors, and matrix multiplication. To understand and implement AI models, you must know Linear Algebra, Probability, and Statistics. A good understanding of derivatives and integrals is necessary as well. Statistics are empirical, and tend to come up a lot. Make sure you are familiar with Gaussian Distributions, Means, and Standard Deviations as they are the fundamentals of AI Engineering. You must also have a firm understanding of Probability as this can help you understand models such as:

• Gaussian Mixture Models
• Naive Bayes
• Hidden Markov Models

The next technical skill you need is in-depth knowledge of algorithm theory and how algorithms work. To completely grasp and understand AI, you will need to know subjects such as the Gradient Descent, Quadratic Programming, Partial Differential Equations, Lagrange, and so on. Unlike front-end development, Machine Learning and Artificial Intelligence are much more math-intensive.  

Natural Language Processing Skills 

NLP incorporates two major fragments of Machine Learning and Artificial Intelligence: Linguistics and Computer Science. As an AI Engineer, the possibility of working with either text, audio, or video is highly probable. Therefore, it is important to have a good grasp of libraries such as Gensim, NLTK, and techniques such as word2vec, Sentimental Analysis, and Summarization. 

Deep Learning and Neural Networks Skills 

While working as an AI Engineer, you might need Machine Learning for assignments that are too complex for humans to code directly. This is where Neural Networks come in. Neural Networks are inspired by the human brain and identify numerical patterns based on sensory data. 

Artificial Intelligence has advanced from single-layer Neural Networks to Deep Learning Neural Networks. Data is passed through manifolds for complex pattern recognition in Deep Learning Neural Networks. They are the most accurate way of approaching complex problems such as Translation, Speech Recognition, and Image Classification. 

Aside from technical skills, AI Engineers also require business and non-technical skills to successfully navigate within companies and organizations.

Conclusion 

When it comes to the best jobs for the future, only a few industries stand out as much as Artificial Intelligence and AI Engineering. The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. The proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these will help engineers become leading AI industry experts, and also aid them in achieving a fulfilling and ever-growing career in the field.  

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